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        If you use plots from MultiQC in a publication or presentation, please cite:

        MultiQC: Summarize analysis results for multiple tools and samples in a single report
        Philip Ewels, Måns Magnusson, Sverker Lundin and Max Käller
        Bioinformatics (2016)
        doi: 10.1093/bioinformatics/btw354
        PMID: 27312411
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        Tool Citations

        Please remember to cite the tools that you use in your analysis.

        To help with this, you can download publication details of the tools mentioned in this report:

        About MultiQC

        This report was generated using MultiQC, version 1.27

        You can see a YouTube video describing how to use MultiQC reports here: https://youtu.be/qPbIlO_KWN0

        For more information about MultiQC, including other videos and extensive documentation, please visit http://multiqc.info

        You can report bugs, suggest improvements and find the source code for MultiQC on GitHub: https://github.com/MultiQC/MultiQC

        MultiQC is published in Bioinformatics:

        MultiQC: Summarize analysis results for multiple tools and samples in a single report
        Philip Ewels, Måns Magnusson, Sverker Lundin and Max Käller
        Bioinformatics (2016)
        doi: 10.1093/bioinformatics/btw354
        PMID: 27312411

        A modular tool to aggregate results from bioinformatics analyses across many samples into a single report.

        This report has been generated by the Joon-Klaps/viralgenie analysis pipeline. For information about how to interpret these results, please see the documentation.
        Report generated on 2025-06-11, 17:18 CEST based on data in:
        • /scratch/leuven/344/vsc34477/work/f2/84cf67ebee7686e6379a3526cae230/multiqc_files
        • /vsc-hard-mounts/leuven-data/344/vsc34477/LVE-BIO2-PIPELINE/viralgenie/assets/multiqc_config.yml

        General Statistics

        Showing 30 samples.

        Created with MultiQC

        Samples with too few reads

        Samples that did not have the minimum number of reads (<1) after trimming, complexity filtering & host removal.

        Showing 2/2 rows.
        SampleNumber of reads
        ERX5701202
        0
        SRX19260942
        0

        SAMPLE: FastQC (Raw)

        Version: 0.12.1

        Quality control tool for high throughput sequencing data.URL: http://www.bioinformatics.babraham.ac.uk/projects/fastqc

        Sequence Counts

        Sequence counts for each sample. Duplicate read counts are an estimate only.

        This plot show the total number of reads, broken down into unique and duplicate if possible (only more recent versions of FastQC give duplicate info).

        You can read more about duplicate calculation in the FastQC documentation. A small part has been copied here for convenience:

        Only sequences which first appear in the first 100,000 sequences in each file are analysed. This should be enough to get a good impression for the duplication levels in the whole file. Each sequence is tracked to the end of the file to give a representative count of the overall duplication level.

        The duplication detection requires an exact sequence match over the whole length of the sequence. Any reads over 75bp in length are truncated to 50bp for this analysis.

        Created with MultiQC

        Sequence Quality Histograms

        The mean quality value across each base position in the read.

        To enable multiple samples to be plotted on the same graph, only the mean quality scores are plotted (unlike the box plots seen in FastQC reports).

        Taken from the FastQC help:

        The y-axis on the graph shows the quality scores. The higher the score, the better the base call. The background of the graph divides the y axis into very good quality calls (green), calls of reasonable quality (orange), and calls of poor quality (red). The quality of calls on most platforms will degrade as the run progresses, so it is common to see base calls falling into the orange area towards the end of a read.

        Created with MultiQC

        Per Sequence Quality Scores

        The number of reads with average quality scores. Shows if a subset of reads has poor quality.

        From the FastQC help:

        The per sequence quality score report allows you to see if a subset of your sequences have universally low quality values. It is often the case that a subset of sequences will have universally poor quality, however these should represent only a small percentage of the total sequences.

        Created with MultiQC

        Per Base Sequence Content

        The proportion of each base position for which each of the four normal DNA bases has been called.

        To enable multiple samples to be shown in a single plot, the base composition data is shown as a heatmap. The colours represent the balance between the four bases: an even distribution should give an even muddy brown colour. Hover over the plot to see the percentage of the four bases under the cursor.

        To see the data as a line plot, as in the original FastQC graph, click on a sample track.

        From the FastQC help:

        Per Base Sequence Content plots out the proportion of each base position in a file for which each of the four normal DNA bases has been called.

        In a random library you would expect that there would be little to no difference between the different bases of a sequence run, so the lines in this plot should run parallel with each other. The relative amount of each base should reflect the overall amount of these bases in your genome, but in any case they should not be hugely imbalanced from each other.

        It's worth noting that some types of library will always produce biased sequence composition, normally at the start of the read. Libraries produced by priming using random hexamers (including nearly all RNA-Seq libraries) and those which were fragmented using transposases inherit an intrinsic bias in the positions at which reads start. This bias does not concern an absolute sequence, but instead provides enrichement of a number of different K-mers at the 5' end of the reads. Whilst this is a true technical bias, it isn't something which can be corrected by trimming and in most cases doesn't seem to adversely affect the downstream analysis.

        Click a sample row to see a line plot for that dataset.
        Rollover for sample name
        Position: -
        %T: -
        %C: -
        %A: -
        %G: -

        Per Sequence GC Content

        The average GC content of reads. Normal random library typically have a roughly normal distribution of GC content.

        From the FastQC help:

        This module measures the GC content across the whole length of each sequence in a file and compares it to a modelled normal distribution of GC content.

        In a normal random library you would expect to see a roughly normal distribution of GC content where the central peak corresponds to the overall GC content of the underlying genome. Since we don't know the GC content of the genome the modal GC content is calculated from the observed data and used to build a reference distribution.

        An unusually shaped distribution could indicate a contaminated library or some other kinds of biased subset. A normal distribution which is shifted indicates some systematic bias which is independent of base position. If there is a systematic bias which creates a shifted normal distribution then this won't be flagged as an error by the module since it doesn't know what your genome's GC content should be.

        Created with MultiQC

        Per Base N Content

        The percentage of base calls at each position for which an N was called.

        From the FastQC help:

        If a sequencer is unable to make a base call with sufficient confidence then it will normally substitute an N rather than a conventional base call. This graph shows the percentage of base calls at each position for which an N was called.

        It's not unusual to see a very low proportion of Ns appearing in a sequence, especially nearer the end of a sequence. However, if this proportion rises above a few percent it suggests that the analysis pipeline was unable to interpret the data well enough to make valid base calls.

        Created with MultiQC

        Sequence Length Distribution

        The distribution of fragment sizes (read lengths) found. See the FastQC help

        Created with MultiQC

        Sequence Duplication Levels

        The relative level of duplication found for every sequence.

        From the FastQC Help:

        In a diverse library most sequences will occur only once in the final set. A low level of duplication may indicate a very high level of coverage of the target sequence, but a high level of duplication is more likely to indicate some kind of enrichment bias (e.g. PCR over amplification). This graph shows the degree of duplication for every sequence in a library: the relative number of sequences with different degrees of duplication.

        Only sequences which first appear in the first 100,000 sequences in each file are analysed. This should be enough to get a good impression for the duplication levels in the whole file. Each sequence is tracked to the end of the file to give a representative count of the overall duplication level.

        The duplication detection requires an exact sequence match over the whole length of the sequence. Any reads over 75bp in length are truncated to 50bp for this analysis.

        In a properly diverse library most sequences should fall into the far left of the plot in both the red and blue lines. A general level of enrichment, indicating broad oversequencing in the library will tend to flatten the lines, lowering the low end and generally raising other categories. More specific enrichments of subsets, or the presence of low complexity contaminants will tend to produce spikes towards the right of the plot.

        Created with MultiQC

        Overrepresented sequences by sample

        The total amount of overrepresented sequences found in each library.

        FastQC calculates and lists overrepresented sequences in FastQ files. It would not be possible to show this for all samples in a MultiQC report, so instead this plot shows the number of sequences categorized as overrepresented.

        Sometimes, a single sequence may account for a large number of reads in a dataset. To show this, the bars are split into two: the first shows the overrepresented reads that come from the single most common sequence. The second shows the total count from all remaining overrepresented sequences.

        From the FastQC Help:

        A normal high-throughput library will contain a diverse set of sequences, with no individual sequence making up a tiny fraction of the whole. Finding that a single sequence is very overrepresented in the set either means that it is highly biologically significant, or indicates that the library is contaminated, or not as diverse as you expected.

        FastQC lists all the sequences which make up more than 0.1% of the total. To conserve memory only sequences which appear in the first 100,000 sequences are tracked to the end of the file. It is therefore possible that a sequence which is overrepresented but doesn't appear at the start of the file for some reason could be missed by this module.

        Created with MultiQC

        Top overrepresented sequences

        Top overrepresented sequences across all samples. The table shows 20 most overrepresented sequences across all samples, ranked by the number of samples they occur in.

        Showing 20/20 rows and 3/3 columns.
        Overrepresented sequenceReportsOccurrences% of all reads
        NNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNN
        6
        2901478
        2.2930%
        GCTGTAGACTGTGCACTTGACCCTCTCTCAGAAACAAAGTGTACGTTGAA
        4
        5862
        0.0046%
        GTGTAAGACGGGCTGCACTTACACCGCAAACCCGTTTAAAAACGATTGTG
        3
        10435
        0.0082%
        CAGCAAAAGCAGGTAGATATTGAAAGATGAGTCTTCTAACCGAGGTCGAA
        3
        8258
        0.0065%
        GTAGAAACAAGGTAGTTTTTTACTCCAGCTCTATGTTGACAAAATGACCA
        3
        6932
        0.0055%
        CACTAGGCCCAATGCCACTTCTGCGGTCACCGTCCCCATCCTGTTGTATA
        3
        5293
        0.0042%
        GAGTGGAGGTCTCCCATCCTCATTACTGCTTCTCCAAGCGAATCTCTGTA
        3
        4075
        0.0032%
        CTGCTGTTCCTGCCGGTACTCTTCCCTCATGGACTCAGGTACTCCTTCCG
        3
        3970
        0.0031%
        GTGTTATCATTCCATTCAAGTCCTCCGATGAGGACCCCAATTGCATTTTT
        3
        3790
        0.0030%
        CCACAGCACTCTGCTGTTCCTGCCGGTACTCTTCCCTCATGGACTCAGGT
        3
        3570
        0.0028%
        CTCATGGAATGGCTAAAGACAAGACCAATCCTGTCACCTCTGACTAAGGG
        3
        2873
        0.0023%
        GTCTTACACCGTGCGGCACAGGCACTAGTACTGATGTCGTATACAGGGCT
        3
        5120
        0.0040%
        GATAAATGCACGCATCCCCCCCGCGAAGGGGGTCAGCGCCCGTCGGCATG
        2
        1816
        0.0014%
        CTCCAAGGGTGTTCACTTTGTTTGCAACTTGCTGTTGTTGTTTGTAACAG
        2
        765
        0.0006%
        NNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNN
        2
        191670
        0.1515%
        GGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGG
        2
        189144
        0.1495%
        GTTACACACCATCAAAACTTATAGAGTACACTGACTTTGCAACATCAGCT
        2
        5411
        0.0043%
        CTATTAAGGTGTTTACAACAGTAGACAACATTAACCTCCACACGCAAGTT
        2
        5312
        0.0042%
        CACTGGTACTGGTCAGGCAATAACAGTCACACCGGAAGCCAATATGGATC
        2
        2857
        0.0023%
        CCTATTATAGTGCGTGAGCCAGAAGATCTCCCTCAGGGTTTTTCGGCTTT
        2
        5030
        0.0040%

        Adapter Content

        The cumulative percentage count of the proportion of your library which has seen each of the adapter sequences at each position.

        Note that only samples with ≥ 0.1% adapter contamination are shown.

        There may be several lines per sample, as one is shown for each adapter detected in the file.

        From the FastQC Help:

        The plot shows a cumulative percentage count of the proportion of your library which has seen each of the adapter sequences at each position. Once a sequence has been seen in a read it is counted as being present right through to the end of the read so the percentages you see will only increase as the read length goes on.

        Created with MultiQC

        Status Checks

        Status for each FastQC section showing whether results seem entirely normal (green), slightly abnormal (orange) or very unusual (red).

        FastQC assigns a status for each section of the report. These give a quick evaluation of whether the results of the analysis seem entirely normal (green), slightly abnormal (orange) or very unusual (red).

        It is important to stress that although the analysis results appear to give a pass/fail result, these evaluations must be taken in the context of what you expect from your library. A 'normal' sample as far as FastQC is concerned is random and diverse. Some experiments may be expected to produce libraries which are biased in particular ways. You should treat the summary evaluations therefore as pointers to where you should concentrate your attention and understand why your library may not look random and diverse.

        Specific guidance on how to interpret the output of each module can be found in the relevant report section, or in the FastQC help.

        In this heatmap, we summarise all of these into a single heatmap for a quick overview. Note that not all FastQC sections have plots in MultiQC reports, but all status checks are shown in this heatmap.

        Created with MultiQC

        SAMPLE: fastp

        Version: 0.23.4

        All-in-one FASTQ preprocessor (QC, adapters, trimming, filtering, splitting...).URL: https://github.com/OpenGene/fastpDOI: 10.1093/bioinformatics/bty560

        Fastp goes through fastq files in a folder and perform a series of quality control and filtering. Quality control and reporting are displayed both before and after filtering, allowing for a clear depiction of the consequences of the filtering process. Notably, the latter can be conducted on a variety of parameters including quality scores, length, as well as the presence of adapters, polyG, or polyX tailing.

        Filtered Reads

        Filtering statistics of sampled reads.

        Created with MultiQC

        Insert Sizes

        Insert size estimation of sampled reads.

        Created with MultiQC

        Sequence Quality

        Average sequencing quality over each base of all reads.

        Created with MultiQC

        GC Content

        Average GC content over each base of all reads.

        Created with MultiQC

        N content

        Average N content over each base of all reads.

        Created with MultiQC

        SAMPLE: FastQC (Post-trimming)

        Version: 0.12.1

        Quality control tool for high throughput sequencing data.URL: http://www.bioinformatics.babraham.ac.uk/projects/fastqc

        Sequence Counts

        Sequence counts for each sample. Duplicate read counts are an estimate only.

        This plot show the total number of reads, broken down into unique and duplicate if possible (only more recent versions of FastQC give duplicate info).

        You can read more about duplicate calculation in the FastQC documentation. A small part has been copied here for convenience:

        Only sequences which first appear in the first 100,000 sequences in each file are analysed. This should be enough to get a good impression for the duplication levels in the whole file. Each sequence is tracked to the end of the file to give a representative count of the overall duplication level.

        The duplication detection requires an exact sequence match over the whole length of the sequence. Any reads over 75bp in length are truncated to 50bp for this analysis.

        Created with MultiQC

        Sequence Quality Histograms

        The mean quality value across each base position in the read.

        To enable multiple samples to be plotted on the same graph, only the mean quality scores are plotted (unlike the box plots seen in FastQC reports).

        Taken from the FastQC help:

        The y-axis on the graph shows the quality scores. The higher the score, the better the base call. The background of the graph divides the y axis into very good quality calls (green), calls of reasonable quality (orange), and calls of poor quality (red). The quality of calls on most platforms will degrade as the run progresses, so it is common to see base calls falling into the orange area towards the end of a read.

        Created with MultiQC

        Per Sequence Quality Scores

        The number of reads with average quality scores. Shows if a subset of reads has poor quality.

        From the FastQC help:

        The per sequence quality score report allows you to see if a subset of your sequences have universally low quality values. It is often the case that a subset of sequences will have universally poor quality, however these should represent only a small percentage of the total sequences.

        Created with MultiQC

        Per Base Sequence Content

        The proportion of each base position for which each of the four normal DNA bases has been called.

        To enable multiple samples to be shown in a single plot, the base composition data is shown as a heatmap. The colours represent the balance between the four bases: an even distribution should give an even muddy brown colour. Hover over the plot to see the percentage of the four bases under the cursor.

        To see the data as a line plot, as in the original FastQC graph, click on a sample track.

        From the FastQC help:

        Per Base Sequence Content plots out the proportion of each base position in a file for which each of the four normal DNA bases has been called.

        In a random library you would expect that there would be little to no difference between the different bases of a sequence run, so the lines in this plot should run parallel with each other. The relative amount of each base should reflect the overall amount of these bases in your genome, but in any case they should not be hugely imbalanced from each other.

        It's worth noting that some types of library will always produce biased sequence composition, normally at the start of the read. Libraries produced by priming using random hexamers (including nearly all RNA-Seq libraries) and those which were fragmented using transposases inherit an intrinsic bias in the positions at which reads start. This bias does not concern an absolute sequence, but instead provides enrichement of a number of different K-mers at the 5' end of the reads. Whilst this is a true technical bias, it isn't something which can be corrected by trimming and in most cases doesn't seem to adversely affect the downstream analysis.

        Click a sample row to see a line plot for that dataset.
        Rollover for sample name
        Position: -
        %T: -
        %C: -
        %A: -
        %G: -

        Per Sequence GC Content

        The average GC content of reads. Normal random library typically have a roughly normal distribution of GC content.

        From the FastQC help:

        This module measures the GC content across the whole length of each sequence in a file and compares it to a modelled normal distribution of GC content.

        In a normal random library you would expect to see a roughly normal distribution of GC content where the central peak corresponds to the overall GC content of the underlying genome. Since we don't know the GC content of the genome the modal GC content is calculated from the observed data and used to build a reference distribution.

        An unusually shaped distribution could indicate a contaminated library or some other kinds of biased subset. A normal distribution which is shifted indicates some systematic bias which is independent of base position. If there is a systematic bias which creates a shifted normal distribution then this won't be flagged as an error by the module since it doesn't know what your genome's GC content should be.

        Created with MultiQC

        Per Base N Content

        The percentage of base calls at each position for which an N was called.

        From the FastQC help:

        If a sequencer is unable to make a base call with sufficient confidence then it will normally substitute an N rather than a conventional base call. This graph shows the percentage of base calls at each position for which an N was called.

        It's not unusual to see a very low proportion of Ns appearing in a sequence, especially nearer the end of a sequence. However, if this proportion rises above a few percent it suggests that the analysis pipeline was unable to interpret the data well enough to make valid base calls.

        Created with MultiQC

        Sequence Length Distribution

        The distribution of fragment sizes (read lengths) found. See the FastQC help

        Created with MultiQC

        Sequence Duplication Levels

        The relative level of duplication found for every sequence.

        From the FastQC Help:

        In a diverse library most sequences will occur only once in the final set. A low level of duplication may indicate a very high level of coverage of the target sequence, but a high level of duplication is more likely to indicate some kind of enrichment bias (e.g. PCR over amplification). This graph shows the degree of duplication for every sequence in a library: the relative number of sequences with different degrees of duplication.

        Only sequences which first appear in the first 100,000 sequences in each file are analysed. This should be enough to get a good impression for the duplication levels in the whole file. Each sequence is tracked to the end of the file to give a representative count of the overall duplication level.

        The duplication detection requires an exact sequence match over the whole length of the sequence. Any reads over 75bp in length are truncated to 50bp for this analysis.

        In a properly diverse library most sequences should fall into the far left of the plot in both the red and blue lines. A general level of enrichment, indicating broad oversequencing in the library will tend to flatten the lines, lowering the low end and generally raising other categories. More specific enrichments of subsets, or the presence of low complexity contaminants will tend to produce spikes towards the right of the plot.

        Created with MultiQC

        Overrepresented sequences by sample

        The total amount of overrepresented sequences found in each library.

        FastQC calculates and lists overrepresented sequences in FastQ files. It would not be possible to show this for all samples in a MultiQC report, so instead this plot shows the number of sequences categorized as overrepresented.

        Sometimes, a single sequence may account for a large number of reads in a dataset. To show this, the bars are split into two: the first shows the overrepresented reads that come from the single most common sequence. The second shows the total count from all remaining overrepresented sequences.

        From the FastQC Help:

        A normal high-throughput library will contain a diverse set of sequences, with no individual sequence making up a tiny fraction of the whole. Finding that a single sequence is very overrepresented in the set either means that it is highly biologically significant, or indicates that the library is contaminated, or not as diverse as you expected.

        FastQC lists all the sequences which make up more than 0.1% of the total. To conserve memory only sequences which appear in the first 100,000 sequences are tracked to the end of the file. It is therefore possible that a sequence which is overrepresented but doesn't appear at the start of the file for some reason could be missed by this module.

        Created with MultiQC

        Top overrepresented sequences

        Top overrepresented sequences across all samples. The table shows 20 most overrepresented sequences across all samples, ranked by the number of samples they occur in.

        Showing 20/20 rows and 3/3 columns.
        Overrepresented sequenceReportsOccurrences% of all reads
        GCTGTAGACTGTGCACTTGACCCTCTCTCAGAAACAAAGTGTACGTTGAA
        4
        4874
        0.0062%
        CAGCAAAAGCAGGTAGATATTGAAAGATGAGTCTTCTAACCGAGGTCGAA
        3
        6331
        0.0080%
        CACTAGGCCCAATGCCACTTCTGCGGTCACCGTCCCCATCCTGTTGTATA
        3
        4382
        0.0056%
        CTGCTGTTCCTGCCGGTACTCTTCCCTCATGGACTCAGGTACTCCTTCCG
        3
        3414
        0.0043%
        GAGTGGAGGTCTCCCATCCTCATTACTGCTTCTCCAAGCGAATCTCTGTA
        3
        3140
        0.0040%
        GTGTTATCATTCCATTCAAGTCCTCCGATGAGGACCCCAATTGCATTTTT
        3
        2843
        0.0036%
        CCACAGCACTCTGCTGTTCCTGCCGGTACTCTTCCCTCATGGACTCAGGT
        3
        2774
        0.0035%
        CTCATGGAATGGCTAAAGACAAGACCAATCCTGTCACCTCTGACTAAGGG
        3
        2346
        0.0030%
        GCTTTGGAGGGAGTGGAGGTCTCCCATCCTCATTACTGCTTCTCCAAGCG
        3
        1766
        0.0022%
        CAGCGAAAGCAGGTAGATATTGAAAGATGAGTCTTCTAACCGAGGTCGAA
        3
        1737
        0.0022%
        AGTCATAAGTTAGTTAAGTCATAAGTTAGTTAAGTCATAAGTTAGTTAAG
        3
        26566
        0.0337%
        CTTATGACTTAACTAACTTATGACTTAACTAACTTATGACTTAACTAACT
        3
        25451
        0.0323%
        GTCATAAGTTAGTTAAGTCATAAGTTAGTTAAGTCATAAGTTAGTTAAGT
        3
        21229
        0.0269%
        ACTTATGACTTAACTAACTTATGACTTAACTAACTTATGACTTAACTAAC
        3
        19905
        0.0253%
        ATTTTATTTAGTGTCTAGAAAAAAATGTGTGACCCACGACCGTAGGAAAC
        3
        9717
        0.0123%
        GTCTTACACCGTGCGGCACAGGCACTAGTACTGATGTCGTATACAGGGCT
        3
        4018
        0.0051%
        GTGTAAGACGGGCTGCACTTACACCGCAAACCCGTTTAAAAACGATTGTG
        3
        8823
        0.0112%
        GTGCCAAGCTCGTCGCCTAAGTCAAATGACTTTAGATCGGCGCCGTAACT
        3
        3643
        0.0046%
        CTTAGATGTCCGGGGCTGCACGCGCGCTACACTGACTGGCTCAGCGTGTG
        2
        79988
        0.1015%
        GTAGAAACAAGGTAGTTTTTTACTCCAGCTCTATGTTGACAAAATGACCA
        2
        4032
        0.0051%

        Adapter Content

        The cumulative percentage count of the proportion of your library which has seen each of the adapter sequences at each position.

        Note that only samples with ≥ 0.1% adapter contamination are shown.

        There may be several lines per sample, as one is shown for each adapter detected in the file.

        From the FastQC Help:

        The plot shows a cumulative percentage count of the proportion of your library which has seen each of the adapter sequences at each position. Once a sequence has been seen in a read it is counted as being present right through to the end of the read so the percentages you see will only increase as the read length goes on.

        Created with MultiQC

        Status Checks

        Status for each FastQC section showing whether results seem entirely normal (green), slightly abnormal (orange) or very unusual (red).

        FastQC assigns a status for each section of the report. These give a quick evaluation of whether the results of the analysis seem entirely normal (green), slightly abnormal (orange) or very unusual (red).

        It is important to stress that although the analysis results appear to give a pass/fail result, these evaluations must be taken in the context of what you expect from your library. A 'normal' sample as far as FastQC is concerned is random and diverse. Some experiments may be expected to produce libraries which are biased in particular ways. You should treat the summary evaluations therefore as pointers to where you should concentrate your attention and understand why your library may not look random and diverse.

        Specific guidance on how to interpret the output of each module can be found in the relevant report section, or in the FastQC help.

        In this heatmap, we summarise all of these into a single heatmap for a quick overview. Note that not all FastQC sections have plots in MultiQC reports, but all status checks are shown in this heatmap.

        Created with MultiQC

        SAMPLE: Kraken2 (Host-removal)

        Taxonomic classification using exact k-mer matches to find the lowest common ancestor (LCA) of a given sequence.URL: https://ccb.jhu.edu/software/krakenDOI: 10.1186/gb-2014-15-3-r46

        Top taxa

        The number of reads falling into the top 5 taxa across different ranks.

        To make this plot, the percentage of each sample assigned to a given taxa is summed across all samples. The counts for these top 5 taxa are then plotted for each of the 9 different taxa ranks. The unclassified count is always shown across all taxa ranks.

        The total number of reads is approximated by dividing the number of unclassified reads by the percentage of the library that they account for. Note that this is only an approximation, and that kraken percentages don't always add to exactly 100%.

        The category "Other" shows the difference between the above total read count and the sum of the read counts in the top 5 taxa shown + unclassified. This should cover all taxa not in the top 5, +/- any rounding errors.

        Note that any taxon that does not exactly fit a taxon rank (eg. - or G2) is ignored.

        Created with MultiQC

        SAMPLE: FastQC (post-Host-removal)

        Version: 0.12.1

        Quality control tool for high throughput sequencing data.URL: http://www.bioinformatics.babraham.ac.uk/projects/fastqc

        Sequence Counts

        Sequence counts for each sample. Duplicate read counts are an estimate only.

        This plot show the total number of reads, broken down into unique and duplicate if possible (only more recent versions of FastQC give duplicate info).

        You can read more about duplicate calculation in the FastQC documentation. A small part has been copied here for convenience:

        Only sequences which first appear in the first 100,000 sequences in each file are analysed. This should be enough to get a good impression for the duplication levels in the whole file. Each sequence is tracked to the end of the file to give a representative count of the overall duplication level.

        The duplication detection requires an exact sequence match over the whole length of the sequence. Any reads over 75bp in length are truncated to 50bp for this analysis.

        Created with MultiQC

        Sequence Quality Histograms

        The mean quality value across each base position in the read.

        To enable multiple samples to be plotted on the same graph, only the mean quality scores are plotted (unlike the box plots seen in FastQC reports).

        Taken from the FastQC help:

        The y-axis on the graph shows the quality scores. The higher the score, the better the base call. The background of the graph divides the y axis into very good quality calls (green), calls of reasonable quality (orange), and calls of poor quality (red). The quality of calls on most platforms will degrade as the run progresses, so it is common to see base calls falling into the orange area towards the end of a read.

        Created with MultiQC

        Per Sequence Quality Scores

        The number of reads with average quality scores. Shows if a subset of reads has poor quality.

        From the FastQC help:

        The per sequence quality score report allows you to see if a subset of your sequences have universally low quality values. It is often the case that a subset of sequences will have universally poor quality, however these should represent only a small percentage of the total sequences.

        Created with MultiQC

        Per Base Sequence Content

        The proportion of each base position for which each of the four normal DNA bases has been called.

        To enable multiple samples to be shown in a single plot, the base composition data is shown as a heatmap. The colours represent the balance between the four bases: an even distribution should give an even muddy brown colour. Hover over the plot to see the percentage of the four bases under the cursor.

        To see the data as a line plot, as in the original FastQC graph, click on a sample track.

        From the FastQC help:

        Per Base Sequence Content plots out the proportion of each base position in a file for which each of the four normal DNA bases has been called.

        In a random library you would expect that there would be little to no difference between the different bases of a sequence run, so the lines in this plot should run parallel with each other. The relative amount of each base should reflect the overall amount of these bases in your genome, but in any case they should not be hugely imbalanced from each other.

        It's worth noting that some types of library will always produce biased sequence composition, normally at the start of the read. Libraries produced by priming using random hexamers (including nearly all RNA-Seq libraries) and those which were fragmented using transposases inherit an intrinsic bias in the positions at which reads start. This bias does not concern an absolute sequence, but instead provides enrichement of a number of different K-mers at the 5' end of the reads. Whilst this is a true technical bias, it isn't something which can be corrected by trimming and in most cases doesn't seem to adversely affect the downstream analysis.

        Click a sample row to see a line plot for that dataset.
        Rollover for sample name
        Position: -
        %T: -
        %C: -
        %A: -
        %G: -

        Per Sequence GC Content

        The average GC content of reads. Normal random library typically have a roughly normal distribution of GC content.

        From the FastQC help:

        This module measures the GC content across the whole length of each sequence in a file and compares it to a modelled normal distribution of GC content.

        In a normal random library you would expect to see a roughly normal distribution of GC content where the central peak corresponds to the overall GC content of the underlying genome. Since we don't know the GC content of the genome the modal GC content is calculated from the observed data and used to build a reference distribution.

        An unusually shaped distribution could indicate a contaminated library or some other kinds of biased subset. A normal distribution which is shifted indicates some systematic bias which is independent of base position. If there is a systematic bias which creates a shifted normal distribution then this won't be flagged as an error by the module since it doesn't know what your genome's GC content should be.

        Created with MultiQC

        Per Base N Content

        The percentage of base calls at each position for which an N was called.

        From the FastQC help:

        If a sequencer is unable to make a base call with sufficient confidence then it will normally substitute an N rather than a conventional base call. This graph shows the percentage of base calls at each position for which an N was called.

        It's not unusual to see a very low proportion of Ns appearing in a sequence, especially nearer the end of a sequence. However, if this proportion rises above a few percent it suggests that the analysis pipeline was unable to interpret the data well enough to make valid base calls.

        Created with MultiQC

        Sequence Length Distribution

        The distribution of fragment sizes (read lengths) found. See the FastQC help

        Created with MultiQC

        Sequence Duplication Levels

        The relative level of duplication found for every sequence.

        From the FastQC Help:

        In a diverse library most sequences will occur only once in the final set. A low level of duplication may indicate a very high level of coverage of the target sequence, but a high level of duplication is more likely to indicate some kind of enrichment bias (e.g. PCR over amplification). This graph shows the degree of duplication for every sequence in a library: the relative number of sequences with different degrees of duplication.

        Only sequences which first appear in the first 100,000 sequences in each file are analysed. This should be enough to get a good impression for the duplication levels in the whole file. Each sequence is tracked to the end of the file to give a representative count of the overall duplication level.

        The duplication detection requires an exact sequence match over the whole length of the sequence. Any reads over 75bp in length are truncated to 50bp for this analysis.

        In a properly diverse library most sequences should fall into the far left of the plot in both the red and blue lines. A general level of enrichment, indicating broad oversequencing in the library will tend to flatten the lines, lowering the low end and generally raising other categories. More specific enrichments of subsets, or the presence of low complexity contaminants will tend to produce spikes towards the right of the plot.

        Created with MultiQC

        Overrepresented sequences by sample

        The total amount of overrepresented sequences found in each library.

        FastQC calculates and lists overrepresented sequences in FastQ files. It would not be possible to show this for all samples in a MultiQC report, so instead this plot shows the number of sequences categorized as overrepresented.

        Sometimes, a single sequence may account for a large number of reads in a dataset. To show this, the bars are split into two: the first shows the overrepresented reads that come from the single most common sequence. The second shows the total count from all remaining overrepresented sequences.

        From the FastQC Help:

        A normal high-throughput library will contain a diverse set of sequences, with no individual sequence making up a tiny fraction of the whole. Finding that a single sequence is very overrepresented in the set either means that it is highly biologically significant, or indicates that the library is contaminated, or not as diverse as you expected.

        FastQC lists all the sequences which make up more than 0.1% of the total. To conserve memory only sequences which appear in the first 100,000 sequences are tracked to the end of the file. It is therefore possible that a sequence which is overrepresented but doesn't appear at the start of the file for some reason could be missed by this module.

        Created with MultiQC

        Top overrepresented sequences

        Top overrepresented sequences across all samples. The table shows 20 most overrepresented sequences across all samples, ranked by the number of samples they occur in.

        Showing 20/20 rows and 3/3 columns.
        Overrepresented sequenceReportsOccurrences% of all reads
        ATTTTATTTAGTGTCTAGAAAAAAATGTGTGACCCACGACCGTAGGAAAC
        4
        9865
        0.0231%
        CTTATGACTTAACTAACTTATGACTTAACTAACTTATGACTTAACTAACT
        4
        25658
        0.0601%
        AGTCATAAGTTAGTTAAGTCATAAGTTAGTTAAGTCATAAGTTAGTTAAG
        4
        26758
        0.0627%
        ACTTATGACTTAACTAACTTATGACTTAACTAACTTATGACTTAACTAAC
        4
        20084
        0.0471%
        GTCATAAGTTAGTTAAGTCATAAGTTAGTTAAGTCATAAGTTAGTTAAGT
        4
        21443
        0.0502%
        ACTATATACTGATCAATATTATCTCTATGAATCCTAAAATAATCATACAG
        4
        11737
        0.0275%
        GCTGTAGACTGTGCACTTGACCCTCTCTCAGAAACAAAGTGTACGTTGAA
        4
        4896
        0.0115%
        CAGCAAAAGCAGGTAGATATTGAAAGATGAGTCTTCTAACCGAGGTCGAA
        3
        6502
        0.0152%
        CACTAGGCCCAATGCCACTTCTGCGGTCACCGTCCCCATCCTGTTGTATA
        3
        4045
        0.0095%
        GAGTGGAGGTCTCCCATCCTCATTACTGCTTCTCCAAGCGAATCTCTGTA
        3
        3193
        0.0075%
        CTGCTGTTCCTGCCGGTACTCTTCCCTCATGGACTCAGGTACTCCTTCCG
        3
        3326
        0.0078%
        CCACAGCACTCTGCTGTTCCTGCCGGTACTCTTCCCTCATGGACTCAGGT
        3
        2948
        0.0069%
        GTGTTATCATTCCATTCAAGTCCTCCGATGAGGACCCCAATTGCATTTTT
        3
        2961
        0.0069%
        CTCATGGAATGGCTAAAGACAAGACCAATCCTGTCACCTCTGACTAAGGG
        3
        2190
        0.0051%
        GCTTTGGAGGGAGTGGAGGTCTCCCATCCTCATTACTGCTTCTCCAAGCG
        3
        1645
        0.0039%
        GTGTATCACTAAAGGATTCAAGATTTACACAGAAGTAACCGAGCAGGTCA
        3
        10499
        0.0246%
        GGTAAGGACTCTCCCGCGATCACGCGTGAAGAAGCTCTGGCTATGATCAA
        3
        13709
        0.0321%
        GTGATACACTGTGTCAAAACTGGATGTTTAGAATACCTATGTAGAATATG
        3
        11115
        0.0260%
        GTCTTACACCGTGCGGCACAGGCACTAGTACTGATGTCGTATACAGGGCT
        3
        4288
        0.0100%
        GTGTAAGACGGGCTGCACTTACACCGCAAACCCGTTTAAAAACGATTGTG
        3
        8787
        0.0206%

        Adapter Content

        The cumulative percentage count of the proportion of your library which has seen each of the adapter sequences at each position.

        Note that only samples with ≥ 0.1% adapter contamination are shown.

        There may be several lines per sample, as one is shown for each adapter detected in the file.

        From the FastQC Help:

        The plot shows a cumulative percentage count of the proportion of your library which has seen each of the adapter sequences at each position. Once a sequence has been seen in a read it is counted as being present right through to the end of the read so the percentages you see will only increase as the read length goes on.

        Created with MultiQC

        Status Checks

        Status for each FastQC section showing whether results seem entirely normal (green), slightly abnormal (orange) or very unusual (red).

        FastQC assigns a status for each section of the report. These give a quick evaluation of whether the results of the analysis seem entirely normal (green), slightly abnormal (orange) or very unusual (red).

        It is important to stress that although the analysis results appear to give a pass/fail result, these evaluations must be taken in the context of what you expect from your library. A 'normal' sample as far as FastQC is concerned is random and diverse. Some experiments may be expected to produce libraries which are biased in particular ways. You should treat the summary evaluations therefore as pointers to where you should concentrate your attention and understand why your library may not look random and diverse.

        Specific guidance on how to interpret the output of each module can be found in the relevant report section, or in the FastQC help.

        In this heatmap, we summarise all of these into a single heatmap for a quick overview. Note that not all FastQC sections have plots in MultiQC reports, but all status checks are shown in this heatmap.

        Created with MultiQC

        SAMPLE: Kraken2 (Diversity)

        Taxonomic classification using exact k-mer matches to find the lowest common ancestor (LCA) of a given sequence.URL: https://ccb.jhu.edu/software/krakenDOI: 10.1186/gb-2014-15-3-r46

        Top taxa

        The number of reads falling into the top 5 taxa across different ranks.

        To make this plot, the percentage of each sample assigned to a given taxa is summed across all samples. The counts for these top 5 taxa are then plotted for each of the 9 different taxa ranks. The unclassified count is always shown across all taxa ranks.

        The total number of reads is approximated by dividing the number of unclassified reads by the percentage of the library that they account for. Note that this is only an approximation, and that kraken percentages don't always add to exactly 100%.

        The category "Other" shows the difference between the above total read count and the sum of the read counts in the top 5 taxa shown + unclassified. This should cover all taxa not in the top 5, +/- any rounding errors.

        Note that any taxon that does not exactly fit a taxon rank (eg. - or G2) is ignored.

        Created with MultiQC

        Duplication rate of top species

        The duplication rate of minimizer falling into the top 5 species

        To make this plot, the minimizer duplication rate is computed for the top 5 most abundant species in all samples.

        The minimizer duplication rate is defined as: duplication rate = (total number of minimizers / number of distinct minimizers)

        A low coverage and high duplication rate (>> 1) is often sign of read stacking, which probably indicates of false positive hit.

        Created with MultiQC

        SAMPLE: Kaiju (Diversity)

        Taxonomic classification for metagenomics.URL: http://kaiju.binf.ku.dkDOI: 10.1038/ncomms11257

        Top taxa

        The number of reads falling into the top 5 taxa across different ranks.

        To make this plot, the percentage of each sample assigned to a given taxa is summed across all samples. The counts for these top five taxa are then plotted for each of the taxa ranks found in logs. The unclassified count is always shown across all taxa ranks. The 'Cannot be assigned' count correspond to reads classified but not at this taxa rank.

        The category "Other" shows the difference between the above total assingned read count and the sum of the read counts in the top 5 taxa shown. This should cover all taxa not in the top 5.

        Created with MultiQC

        SAMPLE: Quast (Spades)

        Quality assessment tool for genome assemblies.URL: http://quast.bioinf.spbau.ruDOI: 10.1093/bioinformatics/btt086

        Assembly Statistics

        Showing 28 samples.

        Created with MultiQC

        Number of Contigs

        This plot shows the number of contigs found for each assembly, broken down by length.

        Created with MultiQC

        SAMPLE: Quast (Megahit)

        Quality assessment tool for genome assemblies.URL: http://quast.bioinf.spbau.ruDOI: 10.1093/bioinformatics/btt086

        Assembly Statistics

        Showing 28 samples.

        Created with MultiQC

        Number of Contigs

        This plot shows the number of contigs found for each assembly, broken down by length.

        Created with MultiQC

        CLUSTER: Samtools Stats (Raw)

        Version: 1.19.2 HTSlib: 1.19.1

        Toolkit for interacting with BAM/CRAM files.URL: http://www.htslib.orgDOI: 10.1093/bioinformatics/btp352

        Percent mapped

        Alignment metrics from samtools stats; mapped vs. unmapped reads vs. reads mapped with MQ0.

        For a set of samples that have come from the same multiplexed library, similar numbers of reads for each sample are expected. Large differences in numbers might indicate issues during the library preparation process. Whilst large differences in read numbers may be controlled for in downstream processings (e.g. read count normalisation), you may wish to consider whether the read depths achieved have fallen below recommended levels depending on the applications.

        Low alignment rates could indicate contamination of samples (e.g. adapter sequences), low sequencing quality or other artefacts. These can be further investigated in the sequence level QC (e.g. from FastQC).

        Reads mapped with MQ0 often indicate that the reads are ambiguously mapped to multiple locations in the reference sequence. This can be due to repetitive regions in the genome, the presence of alternative contigs in the reference, or due to reads that are too short to be uniquely mapped. These reads are often filtered out in downstream analyses.

        Created with MultiQC

        Alignment stats

        This module parses the output from samtools stats. All numbers in millions.

        Showing 78 samples.

        Created with MultiQC

        CLUSTER: Picard

        Tools for manipulating high-throughput sequencing data.URL: http://broadinstitute.github.io/picard

        Mark Duplicates

        Number of reads, categorised by duplication state. Pair counts are doubled - see help text for details.

        The table in the Picard metrics file contains some columns referring read pairs and some referring to single reads.

        To make the numbers in this plot sum correctly, values referring to pairs are doubled according to the scheme below:

        • READS_IN_DUPLICATE_PAIRS = 2 * READ_PAIR_DUPLICATES
        • READS_IN_UNIQUE_PAIRS = 2 * (READ_PAIRS_EXAMINED - READ_PAIR_DUPLICATES)
        • READS_IN_UNIQUE_UNPAIRED = UNPAIRED_READS_EXAMINED - UNPAIRED_READ_DUPLICATES
        • READS_IN_DUPLICATE_PAIRS_OPTICAL = 2 * READ_PAIR_OPTICAL_DUPLICATES
        • READS_IN_DUPLICATE_PAIRS_NONOPTICAL = READS_IN_DUPLICATE_PAIRS - READS_IN_DUPLICATE_PAIRS_OPTICAL
        • READS_IN_DUPLICATE_UNPAIRED = UNPAIRED_READ_DUPLICATES
        • READS_UNMAPPED = UNMAPPED_READS
        Created with MultiQC

        CLUSTER: Samtools Stats (Post-dedup)

        Version: 1.19.2 HTSlib: 1.19.1

        Toolkit for interacting with BAM/CRAM files.URL: http://www.htslib.orgDOI: 10.1093/bioinformatics/btp352

        Percent mapped

        Alignment metrics from samtools stats; mapped vs. unmapped reads vs. reads mapped with MQ0.

        For a set of samples that have come from the same multiplexed library, similar numbers of reads for each sample are expected. Large differences in numbers might indicate issues during the library preparation process. Whilst large differences in read numbers may be controlled for in downstream processings (e.g. read count normalisation), you may wish to consider whether the read depths achieved have fallen below recommended levels depending on the applications.

        Low alignment rates could indicate contamination of samples (e.g. adapter sequences), low sequencing quality or other artefacts. These can be further investigated in the sequence level QC (e.g. from FastQC).

        Reads mapped with MQ0 often indicate that the reads are ambiguously mapped to multiple locations in the reference sequence. This can be due to repetitive regions in the genome, the presence of alternative contigs in the reference, or due to reads that are too short to be uniquely mapped. These reads are often filtered out in downstream analyses.

        Created with MultiQC

        Alignment stats

        This module parses the output from samtools stats. All numbers in millions.

        Showing 77 samples.

        Created with MultiQC

        Flagstat

        This module parses the output from samtools flagstat

        Showing 77 samples.

        Created with MultiQC

        CLUSTER: mosdepth

        Fast BAM/CRAM depth calculation for WGS, exome, or targeted sequencing.URL: https://github.com/brentp/mosdepthDOI: 10.1093/bioinformatics/btx699

        Cumulative coverage distribution

        Proportion of bases in the reference genome with, at least, a given depth of coverage. Calculated across the entire genome length

        For a set of DNA or RNA reads mapped to a reference sequence, such as a genome or transcriptome, the depth of coverage at a given base position is the number of high-quality reads that map to the reference at that position, while the breadth of coverage is the fraction of the reference sequence to which reads have been mapped with at least a given depth of coverage (Sims et al. 2014).

        Defining coverage breadth in terms of coverage depth is useful, because sequencing experiments typically require a specific minimum depth of coverage over the region of interest (Sims et al. 2014), so the extent of the reference sequence that is amenable to analysis is constrained to lie within regions that have sufficient depth. With inadequate sequencing breadth, it can be difficult to distinguish the absence of a biological feature (such as a gene) from a lack of data (Green 2007).

        For increasing coverage depths (1×, 2×, …, N×), coverage breadth is calculated as the percentage of the reference sequence that is covered by at least that number of reads, then plots coverage breadth (y-axis) against coverage depth (x-axis). This plot shows the relationship between sequencing depth and breadth for each read dataset, which can be used to gauge, for example, the likely effect of a minimum depth filter on the fraction of a genome available for analysis.

        Created with MultiQC

        Average coverage per contig

        Average coverage per contig or chromosome

        Created with MultiQC

        CLUSTER: Bcftools

        Version: 1.18

        Utilities for variant calling and manipulating VCFs and BCFs.URL: https://samtools.github.io/bcftoolsDOI: 10.1093/gigascience/giab008

        Variant Substitution Types

        Created with MultiQC

        Variant Quality

        Created with MultiQC

        Indel Distribution

        Created with MultiQC

        Variant depths

        Read depth support distribution for called variants

        Created with MultiQC

        CLUSTER: Total variants (iVar)

        CLUSTER: Total variants (iVar) is calculated from the total number of variants called by iVar.

        Created with MultiQC

        Failed contig quality

        Contigs that are not of minimum size 500 or have more then 50 ambigous bases per 100 kbp were filtered out.

        Showing 12/12 rows and 5/5 columns.
        Idsample nameclusterstepcontig sizeN's %
        SRX6755149_cl56
        SRX6755149
        cl56
        consensus
        138
        0
        SRX6755149_cl64
        SRX6755149
        cl64
        consensus
        128
        0
        SRX6755149_cl71
        SRX6755149
        cl71
        consensus
        139
        0
        SRX6755149_cl72
        SRX6755149
        cl72
        consensus
        130
        0
        SRX6755149_cl109
        SRX6755149
        cl109
        consensus
        401
        0
        SRX13440223_cl32
        SRX13440223
        cl32
        consensus
        128
        0
        SRX17527367_cl5
        SRX17527367
        cl5
        consensus
        135
        0
        SRX17527367_cl8
        SRX17527367
        cl8
        consensus
        263
        0
        SRX19467387_cl11
        SRX19467387
        cl11
        consensus
        163
        0
        SRX26211983_cl3
        SRX26211983
        cl3
        consensus
        314
        0
        SRX28439211_cl4
        SRX28439211
        cl4
        it1
        1317
        52
        SRX28439211_cl35
        SRX28439211
        cl35
        consensus
        267
        0

        Cluster Summary

        Number of identified contig clusters per sample after assembly.

        Created with MultiQC

        Software Versions

        Software Versions lists versions of software tools extracted from file contents.

        GroupSoftwareVersion
        BCFTOOLS_CONSENSUSbcftools1.18
        BCFTOOLS_FILTERbcftools1.18
        BCFTOOLS_SORTbcftools1.18
        BCFTOOLS_STATSbcftools1.18
        BEDTOOLS_MASKFASTAbedtools2.31.1
        BEDTOOLS_MERGEbedtools2.31.1
        BLASTN_QCblast2.14.1+
        BLAST_BLASTNblast2.14.1+
        BLAST_FILTERbiopython1.79
        pandas0.25.3
        python3.8.12
        BLAST_MAKEBLASTDBblast2.15.0+
        BWAMEM2_INDEXbwamem22.2.1
        BWAMEM2_MEMbwamem22.2.1
        samtools1.19.2
        CAT_ASSEMBLERSpigz2.3.4
        CAT_CLUSTERpigz2.3.4
        CHECKV_DOWNLOADDATABASEcheckv1.0.1
        CLUSTER: BcftoolsCLUSTER: Bcftools1.18
        CLUSTER: Samtools Stats (Post-dedup)CLUSTER: Samtools Stats (Post-dedup)1.19.2
        HTSlib1.19.1
        CLUSTER: Samtools Stats (Raw)CLUSTER: Samtools Stats (Raw)1.19.2
        HTSlib1.19.1
        CONTIG_IDXSTATSsamtools1.19.2
        CONTIG_INDEXsamtools1.19.2
        CUSTOM_MPILEUPnumpy1.26.4
        pysam0.22.1
        pysamstats1.1.2
        python3.9.19
        EXTRACT_CLUSTERbiopython1.81
        python3.12.0
        EXTRACT_PRECLUSTERbiopython1.78
        python3.9.1
        FASTPfastp0.23.4
        FASTQC_HOSTfastqc0.12.1
        FASTQC_RAWfastqc0.12.1
        FASTQC_TRIMfastqc0.12.1
        GUNZIP_DBgunzip1.1
        IVAR_CONTIG_CONSENSUSivar1.4
        samtools1.16.1
        IVAR_VARIANTSivar1.4
        samtools1.16.1
        IVAR_VARIANTS_TO_VCFpython3.9.12
        KAIJU_CONTIGkaiju1.10.0
        KAIJU_KAIJUkaiju1.10.0
        KAIJU_KAIJU2KRONAkaiju1.10.0
        KAIJU_KAIJU2TABLEkaiju1.10.0
        KRAKEN2_CONTIGkraken22.1.3
        pigz2.8
        KRAKEN2_HOST_REMOVEkraken22.1.3
        pigz2.8
        KRAKEN2_KRAKEN2kraken22.1.3
        pigz2.8
        KRAKENTOOLS_KREPORT2KRONAkreport2krona.py1.2
        KRONA_CLEANUPsed4.7
        KRONA_KTIMPORTTEXTkrona2.8.1
        MAKE_BED_MASKpython3.9.5
        samtools1.14
        MEGAHITmegahit1.2.9
        MINIMAP2_CONTIG_ALIGNminimap22.28-r1209
        MINIMAP2_CONTIG_INDEXminimap22.28-r1209
        MMSEQS_CLUSTERmmseqs15.6f452
        MMSEQS_CREATEANNOTATIONDBmmseqs15.6f452
        MMSEQS_CREATEDBmmseqs15.6f452
        MMSEQS_CREATETSVmmseqs15.6f452
        MMSEQS_EASYSEARCHmmseqs15.6f452
        MOSDEPTHmosdepth0.3.8
        NOCOV_TO_REFERENCEbiopython1.84
        matplotlib3.9.2
        numpy2.1.1
        python3.12.5
        PICARD_COLLECTMULTIPLEMETRICSpicard3.1.1
        PICARD_MARKDUPLICATESpicard3.1.1
        PRINSEQ_CONTIGprinseqplusplus1.2
        QUASTquast5.2.0
        QUAST_QCquast5.2.0
        RENAME_FASTA_HEADER_CALLED_CONSENSUSsed4.7
        RENAME_FASTA_HEADER_CONTIG_CONSENSUSsed4.7
        RENAME_FASTA_HEADER_SINGLETONsed4.7
        SAMPLE: FastQC (Post-trimming)SAMPLE: FastQC (Post-trimming)0.12.1
        SAMPLE: FastQC (Raw)SAMPLE: FastQC (Raw)0.12.1
        SAMPLE: FastQC (post-Host-removal)SAMPLE: FastQC (post-Host-removal)0.12.1
        SAMPLE: fastpSAMPLE: fastp0.23.4
        SAMTOOLS_FAIDXsamtools1.19.2
        SAMTOOLS_FLAGSTATsamtools1.19.2
        SAMTOOLS_IDXSTATSsamtools1.19.2
        SAMTOOLS_INDEXsamtools1.19.2
        SAMTOOLS_STATSsamtools1.19.2
        SPADESspades4.0.0
        SSPACE_BASICsspace_base2.1.1
        TABIX_TABIXtabix1.19.1
        UNTAR_DBuntar1.3
        WorkflowJoon-Klaps/viralgeniev0.1.3dev
        Nextflow25.04.3

        Joon-Klaps/viralgenie Methods Description

        Suggested text and references to use when describing pipeline usage within the methods section of a publication.URL: https://github.com/Joon-Klaps/viralgenie

        Methods

        Data was processed using Joon-Klaps/viralgenie v0.1.3dev of the nf-core collection of workflows (Ewels et al., 2020), utilising reproducible software environments from the Bioconda (Grüning et al., 2018) and Biocontainers (da Veiga Leprevost et al., 2017) projects.

        The pipeline was executed with Nextflow v25.04.3 (Di Tommaso et al., 2017) with the following command:

        nextflow run /vsc-hard-mounts/leuven-data/344/vsc34477/LVE-BIO2-PIPELINE/viralgenie -ansi-log false --cluster_method mmseqs-cluster --keep_unclassified false -with-tower -resume -profile singularity,wice,vsc_kul_uhasselt --input samplesheet.csv --outdir results

        Tools used in the workflow included: Viralgenie (Klaps et al.) nf-core (Ewels et al. 2020) Nextflow (Di Tommaso et al. 2017) Bbduk (Bushnell 2022) BCFtools (Danecek et al. 2021) BLAST+ (Camacho et al. 2009) Bowtie2 (Langmead and Salzberg 2012) BWA-MEM (Li 2013) BWA-MEM2 (Vasimuddin et al. 2019) CD-HIT (Fu et al. 2012) CheckV (Nayfach et al. 2021) FastQC (Andrews 2010) fastp (Chen et al. 2018) HUMID (Laros and van den Berg) iVar (Grubaugh et al. 2019) Kaiju (Menzel et al. 2016) Kraken2 (Wood et al. 2019) Leiden Algorithm (Traag et al. 2019) Mash (Ondov et al. 2016) MEGAHIT (Li et al. 2016) Minimap2 (Li 2018) MMseqs2 (Steinegger and Söding 2017) Mosdepth (Pedersen and Quinlan 2018) MultiQC (Ewels et al. 2016) Picard (Broad Institute) QUAST (Gurevich et al. 2013) SAMtools (Li 2011) SPAdes (Bankevich et al. 2012) SSPACE Basic (Boetzer et al. 2011) Trimmomatic (Bolger et al. 2014) Trinity (Haas et al. 2013) UMI-tools (Smith et al. 2017) vRhyme (Kieft et al. 2022) VSEARCH (Rognes et al. 2016) Anaconda (Anaconda 2016) Bioconda (Grüning et al. 2018) BioContainers (da Veiga Leprevost et al. 2017) Docker (Merkel 2014) Singularity (Kurtzer et al. 2017) .

        References

        • Klaps J, Lemey P, Kafetzopoulou L. Viralgenie: A metagenomics analysis pipeline for eukaryotic viruses. __Github__ https://github.com/Joon-Klaps/viralgenie.
        • Ewels PA, Peltzer A, Fillinger S, Patel H, Alneberg J, Wilm A, Garcia MU, Di Tommaso P, Nahnsen S. The nf-core framework for community-curated bioinformatics pipelines. Nat Biotechnol. 2020 Mar;38(3):276-278. doi: 10.1038/s41587-020-0439-x. PubMed PMID: 32055031.
        • Di Tommaso P, Chatzou M, Floden EW, Barja PP, Palumbo E, Notredame C. Nextflow enables reproducible computational workflows. Nat Biotechnol. 2017 Apr 11;35(4):316-319. doi: 10.1038/nbt.3820. PubMed PMID: 28398311.
        • Bushnell B. (2022) BBMap, URL: http://sourceforge.net/projects/bbmap/
        • Danecek, Petr et al. “Twelve years of SAMtools and BCFtools.” GigaScience vol. 10,2 (2021): giab008. doi:10.1093/gigascience/giab008
        • Camacho, Christiam et al. “BLAST+: architecture and applications.” BMC bioinformatics vol. 10 421. 15 Dec. 2009, doi:10.1186/1471-2105-10-421
        • Langmead, Ben, and Steven L Salzberg. “Fast gapped-read alignment with Bowtie 2.” Nature methods vol. 9,4 357-9. 4 Mar. 2012, doi:10.1038/nmeth.1923
        • Li H. (2013) Aligning sequence reads, clone sequences and assembly contigs with BWA-MEM. arXiv:1303.3997v2.
        • M. Vasimuddin, S. Misra, H. Li and S. Aluru, 'Efficient Architecture-Aware Acceleration of BWA-MEM for Multicore Systems,' 2019 IEEE International Parallel and Distributed Processing Symposium (IPDPS), Rio de Janeiro, Brazil, 2019, pp. 314-324, doi: 10.1109/IPDPS.2019.00041.
        • Fu, Limin et al. “CD-HIT: accelerated for clustering the next-generation sequencing data.” Bioinformatics (Oxford, England) vol. 28,23 (2012): 3150-2. doi:10.1093/bioinformatics/bts565
        • Nayfach, Stephen et al. “CheckV assesses the quality and completeness of metagenome-assembled viral genomes.” Nature biotechnology vol. 39,5 (2021): 578-585. doi:10.1038/s41587-020-00774-7
        • Andrews, S. (2010). FastQC: A Quality Control Tool for High Throughput Sequence Data [Online].
        • Chen, Shifu et al. “fastp: an ultra-fast all-in-one FASTQ preprocessor.” Bioinformatics (Oxford, England) vol. 34,17 (2018): i884-i890. doi:10.1093/bioinformatics/bty560
        • Laros J, van den Berg R, __Github__ https://github.com/jfjlaros/HUMID
        • Grubaugh, Nathan D et al. “An amplicon-based sequencing framework for accurately measuring intrahost virus diversity using PrimalSeq and iVar.” Genome biology vol. 20,1 8. 8 Jan. 2019, doi:10.1186/s13059-018-1618-7
        • Menzel, Peter et al. “Fast and sensitive taxonomic classification for metagenomics with Kaiju.” Nature communications vol. 7 11257. 13 Apr. 2016, doi:10.1038/ncomms11257
        • Wood, Derrick E., Jennifer Lu, and Ben Langmead. 2019. Improved Metagenomic Analysis with Kraken 2. Genome Biology 20 (1): 257. doi: 10.1186/s13059-019-1891-0.
        • Traag, V A et al. “From Louvain to Leiden: guaranteeing well-connected communities.” Scientific reports vol. 9,1 5233. 26 Mar. 2019, doi:10.1038/s41598-019-41695-z
        • Ondov, Brian D et al. “Mash: fast genome and metagenome distance estimation using MinHash.” Genome biology vol. 17,1 132. 20 Jun. 2016, doi:10.1186/s13059-016-0997-x
        • Li, Dinghua et al. “MEGAHIT v1.0: A fast and scalable metagenome assembler driven by advanced methodologies and community practices.” Methods (San Diego, Calif.) vol. 102 (2016): 3-11. doi:10.1016/j.ymeth.2016.02.020
        • Li, Heng. “Minimap2: pairwise alignment for nucleotide sequences.” Bioinformatics (Oxford, England) vol. 34,18 (2018): 3094-3100. doi:10.1093/bioinformatics/bty191
        • Steinegger, Martin, and Johannes Söding. “MMseqs2 enables sensitive protein sequence searching for the analysis of massive data sets.” Nature biotechnology vol. 35,11 (2017): 1026-1028. doi:10.1038/nbt.3988
        • Pedersen, Brent S, and Aaron R Quinlan. “Mosdepth: quick coverage calculation for genomes and exomes.” Bioinformatics (Oxford, England) vol. 34,5 (2018): 867-868. doi:10.1093/bioinformatics/btx699
        • Ewels, Philip et al. “MultiQC: summarize analysis results for multiple tools and samples in a single report.” Bioinformatics (Oxford, England) vol. 32,19 (2016): 3047-8. doi:10.1093/bioinformatics/btw354
        • Gurevich, Alexey et al. “QUAST: quality assessment tool for genome assemblies.” Bioinformatics (Oxford, England) vol. 29,8 (2013): 1072-5. doi:10.1093/bioinformatics/btt086
        • Li H. A statistical framework for SNP calling, mutation discovery, association mapping and population genetical parameter estimation from sequencing data. Bioinformatics. 2011 Nov 1;27(21):2987-93. doi: 10.1093/bioinformatics/btr509. Epub 2011 Sep 8. PMID: 21903627; PMCID: PMC3198575.
        • Bankevich, Anton et al. “SPAdes: a new genome assembly algorithm and its applications to single-cell sequencing.” Journal of computational biology : a journal of computational molecular cell biology vol. 19,5 (2012): 455-77. doi:10.1089/cmb.2012.0021
        • Boetzer, Marten et al. “Scaffolding pre-assembled contigs using SSPACE.” Bioinformatics (Oxford, England) vol. 27,4 (2011): 578-9. doi:10.1093/bioinformatics/btq683
        • Bolger, Anthony M et al. “Trimmomatic: a flexible trimmer for Illumina sequence data.” Bioinformatics (Oxford, England) vol. 30,15 (2014): 2114-20. doi:10.1093/bioinformatics/btu170
        • Haas, Brian J et al. “De novo transcript sequence reconstruction from RNA-seq using the Trinity platform for reference generation and analysis.” Nature protocols vol. 8,8 (2013): 1494-512. doi:10.1038/nprot.2013.084
        • Smith, Tom et al. “UMI-tools: modeling sequencing errors in Unique Molecular Identifiers to improve quantification accuracy.” Genome research vol. 27,3 (2017): 491-499. doi:10.1101/gr.209601.116
        • Kieft, Kristopher et al. “vRhyme enables binning of viral genomes from metagenomes.” Nucleic acids research vol. 50,14 (2022): e83. doi:10.1093/nar/gkac341
        • Rognes, Torbjørn et al. “VSEARCH: a versatile open source tool for metagenomics.” PeerJ vol. 4 e2584. 18 Oct. 2016, doi:10.7717/peerj.2584
        • Anaconda Software Distribution. Computer software. Vers. 2-2.4.0. Anaconda, Nov. 2016. Web.
        • Grüning B, Dale R, Sjödin A, Chapman BA, Rowe J, Tomkins-Tinch CH, Valieris R, Köster J; Bioconda Team. Bioconda: sustainable and comprehensive software distribution for the life sciences. Nat Methods. 2018 Jul;15(7):475-476. doi: 10.1038/s41592-018-0046-7. PubMed PMID: 29967506.
        • da Veiga Leprevost F, Grüning B, Aflitos SA, Röst HL, Uszkoreit J, Barsnes H, Vaudel M, Moreno P, Gatto L, Weber J, Bai M, Jimenez RC, Sachsenberg T, Pfeuffer J, Alvarez RV, Griss J, Nesvizhskii AI, Perez-Riverol Y. BioContainers: an open-source and community-driven framework for software standardization. Bioinformatics. 2017 Aug 15;33(16):2580-2582. doi: 10.1093/bioinformatics/btx192. PubMed PMID: 28379341; PubMed Central PMCID: PMC5870671.
        • Merkel, D. (2014). Docker: lightweight linux containers for consistent development and deployment. Linux Journal, 2014(239), 2. doi: 10.5555/2600239.2600241.
        • Kurtzer GM, Sochat V, Bauer MW. Singularity: Scientific containers for mobility of compute. PLoS One. 2017 May 11;12(5):e0177459. doi: 10.1371/journal.pone.0177459. eCollection 2017. PubMed PMID: 28494014; PubMed Central PMCID: PMC5426675.
        Notes:
        • If available, make sure to update the text to include the Zenodo DOI of version of the pipeline used.
        • The command above does not include parameters contained in any configs or profiles that may have been used. Ensure the config file is also uploaded with your publication!
        • You should also cite all software used within this run. Check the "Software Versions" of this report to get version information.

        Joon-Klaps/viralgenie Workflow Summary

        - this information is collected when the pipeline is started.URL: https://github.com/Joon-Klaps/viralgenie

        Input/output options

        input
        samplesheet.csv
        outdir
        results

        Preprocessing options

        arguments_umitools_extract
        --umi-separator ":"

        Polishing

        arguments_extract_precluster
        --keep-unclassified false --merge-strategy lca
        cluster_method
        mmseqs-cluster
        keep_unclassified
        false

        Variant analysis

        arguments_bcftools_mpileup3
        --include 'INFO/DP>=10'
        arguments_custom_mpileup
        --max-depth 80000
        arguments_umitools_dedup
        --umi-separator=':' --method cluster --unmapped-reads use

        Institutional config options

        config_profile_contact
        GitHub: @Joon-Klaps - Email: joon.klaps@kuleuven.be
        config_profile_description
        wice profile for use on the Wice cluster of the VSC HPC.
        config_profile_url
        https://docs.vscentrum.be/en/latest/index.html

        Core Nextflow options

        configFiles
        /vsc-hard-mounts/leuven-data/344/vsc34477/LVE-BIO2-PIPELINE/viralgenie/nextflow.config
        containerEngine
        singularity
        launchDir
        /lustre1/project/stg_00132/jklaps/viralgenie-manuscript/analysis/viralgenie
        profile
        singularity,wice,vsc_kul_uhasselt
        projectDir
        /vsc-hard-mounts/leuven-data/344/vsc34477/LVE-BIO2-PIPELINE/viralgenie
        runName
        boring_morse
        userName
        vsc34477
        workDir
        /scratch/leuven/344/vsc34477/work